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THE ML ENGINEER 🤖
Issue #109
 
This week in Issue #109:
 
 
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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
 
 
 
We'll be speaking at the SF Big Analytics meetup on "Production ML Monitoring: Outliers, Drift, Explainers & Statistical Performance" in February. It will be happening online so feel free to join even if you're not in the area - we will be joining from across the pond!
 
 
 
Following the DS & ML books list, the team at "InsaneApp" has put together a fantastic 100+ free programming books ranging across a broad set of programming languages (Python, C++, C, Java and beyond).
 
 
 
Entrepreneur First CEO Matt Clifford released a podcast conversation with State-of-AI report author Co-author Ian Hogarth to discuss the geopolitics of AI, where they explore how technology collides with politics, culture and society.
 
 
 
"How to Ignore Most Startup Advice and Build a Decent Software Business" by SpaCy and Explosion.ai Co-founder Ines Montani. In this talk Ines shares some of the things she has learned from building a successful software company around commercial developer tools and their open-source library spaCy.
 
 
 
Uber builds multi-sided marketplaces handling millions of trips every day across the globe. This has presented challenges that have required innovative thinking around the interaction between the multiple heterogeneous types of applications that require data flow. In this post they discuss the challenges with polling, and showcase how they introduced a push infrastructure through their framework RAMEN.
 
 
 
 
 
The topic for this week's featured production machine learning libraries is Metadata Management. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:
  • Amundsen - Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
  • DataHub - DataHub is LinkedIn's generalized metadata search & discovery tool.
  • Metacat - Metacat is a unified metadata exploration API service. Metacat focusses on solving these three problems: 1) Federate views of metadata systems. 2) Allow arbitrary metadata storage about data sets. 3) Metadata discovery.
  • ML Metadata - a library for recording and retrieving metadata associated with ML developer and data scientist workflows. Also TensorFlow ML Metadata.
 
If you know of any libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request
 
 
 
 
As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:
 
 
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request
 
 
 
© 2018 The Institute for Ethical AI & Machine Learning